In recent years, with the development of artificial intelligence, Internet Things (IoT) has become a research hotspot in industry and academia. At same time, as derivative tool machine translation based on IoT is constantly being applied to English its teaching. teaching, helping students learn been focus years. Compared human translation, more efficient convenient. However, also some problems....

Typical statistical machine translation systems are batch trained with a given training data and their performances are largely influenced by the amount of data. With the growth of the available data across different domains, it is computationally demanding to perform batch training every time when new data comes. In face of the problem, we propose an efficient phrase table combination method. ...

In statistical machine translation, minimum error rate training (MERT) is a standard method for tuning a single weight with regard to a given development data. However, due to the diversity and uneven distribution of source sentences, there are two problems suffered by this method. First, its performance is highly dependent on the choice of a development set, which may lead to an unstable perfo...

While neural machine translation (NMT) has become the new paradigm, the parameter optimization requires large-scale parallel data which is scarce in many domains and language pairs. In this paper, we address a new translation scenario in which there only exists monolingual corpora and phrase pairs. We propose a new method towards translation with partially aligned sentence pairs which are deriv...

Parallel sentences are crucial for statistical machine translation (SMT). However, they are quite scarce for most language pairs, such as Chinese–Japanese. Many studies have been conducted on extracting parallel sentences from noisy parallel or comparable corpora. We extract Chinese–Japanese parallel sentences from quasi–comparable corpora, which are available in far larger quantities. The task...

When vocabulary teaching is taken into account in EFL classes in our Iranian state primary schools, teachers generally prefer to use classical techniques. The purpose of this study was to investigate the effect of visually-mediated collocations on the elementary EFL learners’ vocabulary learning. In order to conduct this study, 60 students from two classrooms in an elementary class, participate...

Training efficiency is one of the main problems for Neural Machine Translation (NMT). Deep networks, very large data and many training iterations are necessary to achieve state-of-the-art performance for NMT. This results in very high computation cost and slow down research and industrialization. In this paper, we first investigate the instability by randomizations for NMT training, and further...

In this paper, we present a novel training method for a localized phrase-based prediction model for statistical machine translation (SMT). The model predicts blocks with orientation to handle local phrase re-ordering. We use a maximum likelihood criterion to train a log-linear block bigram model which uses realvalued features (e.g. a language model score) as well as binary features based on the...